from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-03 14:07:29.022808
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Thu, 03, Dec, 2020
Time: 14:07:32
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.1602
Nobs: 129.000 HQIC: -44.3447
Log likelihood: 1355.14 FPE: 2.45743e-20
AIC: -45.1554 Det(Omega_mle): 1.25503e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.559602 0.187057 2.992 0.003
L1.Burgenland 0.130037 0.087134 1.492 0.136
L1.Kärnten -0.309228 0.073913 -4.184 0.000
L1.Niederösterreich 0.063675 0.210168 0.303 0.762
L1.Oberösterreich 0.292337 0.174093 1.679 0.093
L1.Salzburg 0.151177 0.088210 1.714 0.087
L1.Steiermark 0.079541 0.125090 0.636 0.525
L1.Tirol 0.172778 0.082948 2.083 0.037
L1.Vorarlberg 0.021195 0.080696 0.263 0.793
L1.Wien -0.134430 0.165621 -0.812 0.417
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.611347 0.239671 2.551 0.011
L1.Burgenland -0.005700 0.111642 -0.051 0.959
L1.Kärnten 0.334674 0.094703 3.534 0.000
L1.Niederösterreich 0.089081 0.269283 0.331 0.741
L1.Oberösterreich -0.218152 0.223061 -0.978 0.328
L1.Salzburg 0.185046 0.113022 1.637 0.102
L1.Steiermark 0.238771 0.160275 1.490 0.136
L1.Tirol 0.141133 0.106279 1.328 0.184
L1.Vorarlberg 0.204423 0.103394 1.977 0.048
L1.Wien -0.556574 0.212207 -2.623 0.009
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.326273 0.080974 4.029 0.000
L1.Burgenland 0.099930 0.037719 2.649 0.008
L1.Kärnten -0.028800 0.031996 -0.900 0.368
L1.Niederösterreich 0.117318 0.090978 1.290 0.197
L1.Oberösterreich 0.280315 0.075362 3.720 0.000
L1.Salzburg -0.014142 0.038185 -0.370 0.711
L1.Steiermark -0.050259 0.054149 -0.928 0.353
L1.Tirol 0.100606 0.035907 2.802 0.005
L1.Vorarlberg 0.140734 0.034932 4.029 0.000
L1.Wien 0.036836 0.071695 0.514 0.607
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.185899 0.095874 1.939 0.053
L1.Burgenland 0.001732 0.044660 0.039 0.969
L1.Kärnten 0.031507 0.037883 0.832 0.406
L1.Niederösterreich 0.056925 0.107720 0.528 0.597
L1.Oberösterreich 0.371921 0.089230 4.168 0.000
L1.Salzburg 0.086586 0.045211 1.915 0.055
L1.Steiermark 0.201508 0.064114 3.143 0.002
L1.Tirol 0.035837 0.042514 0.843 0.399
L1.Vorarlberg 0.112958 0.041360 2.731 0.006
L1.Wien -0.086842 0.084888 -1.023 0.306
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.729735 0.203830 3.580 0.000
L1.Burgenland 0.060857 0.094947 0.641 0.522
L1.Kärnten -0.015105 0.080541 -0.188 0.851
L1.Niederösterreich -0.112023 0.229013 -0.489 0.625
L1.Oberösterreich 0.089037 0.189704 0.469 0.639
L1.Salzburg 0.030389 0.096120 0.316 0.752
L1.Steiermark 0.126696 0.136307 0.929 0.353
L1.Tirol 0.230598 0.090386 2.551 0.011
L1.Vorarlberg 0.033454 0.087932 0.380 0.704
L1.Wien -0.148594 0.180473 -0.823 0.410
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.223631 0.140119 1.596 0.110
L1.Burgenland -0.052047 0.065269 -0.797 0.425
L1.Kärnten -0.017401 0.055366 -0.314 0.753
L1.Niederösterreich 0.171860 0.157431 1.092 0.275
L1.Oberösterreich 0.397942 0.130408 3.052 0.002
L1.Salzburg -0.038846 0.066076 -0.588 0.557
L1.Steiermark -0.056162 0.093701 -0.599 0.549
L1.Tirol 0.203054 0.062134 3.268 0.001
L1.Vorarlberg 0.050429 0.060447 0.834 0.404
L1.Wien 0.130117 0.124063 1.049 0.294
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.253730 0.178792 1.419 0.156
L1.Burgenland 0.058208 0.083283 0.699 0.485
L1.Kärnten -0.083412 0.070647 -1.181 0.238
L1.Niederösterreich -0.126651 0.200881 -0.630 0.528
L1.Oberösterreich -0.073782 0.166400 -0.443 0.657
L1.Salzburg 0.009942 0.084313 0.118 0.906
L1.Steiermark 0.372068 0.119563 3.112 0.002
L1.Tirol 0.544234 0.079283 6.864 0.000
L1.Vorarlberg 0.230625 0.077131 2.990 0.003
L1.Wien -0.178515 0.158303 -1.128 0.259
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.057794 0.205685 0.281 0.779
L1.Burgenland 0.033493 0.095811 0.350 0.727
L1.Kärnten -0.065003 0.081273 -0.800 0.424
L1.Niederösterreich 0.226704 0.231097 0.981 0.327
L1.Oberösterreich 0.033906 0.191430 0.177 0.859
L1.Salzburg 0.228857 0.096995 2.359 0.018
L1.Steiermark 0.174864 0.137547 1.271 0.204
L1.Tirol 0.055153 0.091209 0.605 0.545
L1.Vorarlberg 0.015639 0.088732 0.176 0.860
L1.Wien 0.246578 0.182115 1.354 0.176
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.615561 0.114522 5.375 0.000
L1.Burgenland -0.018796 0.053346 -0.352 0.725
L1.Kärnten -0.004701 0.045252 -0.104 0.917
L1.Niederösterreich -0.070654 0.128671 -0.549 0.583
L1.Oberösterreich 0.293745 0.106585 2.756 0.006
L1.Salzburg 0.006057 0.054005 0.112 0.911
L1.Steiermark 0.011671 0.076584 0.152 0.879
L1.Tirol 0.078916 0.050783 1.554 0.120
L1.Vorarlberg 0.188781 0.049405 3.821 0.000
L1.Wien -0.091836 0.101399 -0.906 0.365
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.095859 -0.053216 0.180836 0.233256 0.006857 0.072319 -0.122540 0.120325
Kärnten 0.095859 1.000000 -0.052898 0.183291 0.087255 -0.166972 0.195124 0.020510 0.270078
Niederösterreich -0.053216 -0.052898 1.000000 0.250510 0.066714 0.162104 0.071057 0.053398 0.356979
Oberösterreich 0.180836 0.183291 0.250510 1.000000 0.255905 0.269141 0.075858 0.066559 0.051265
Salzburg 0.233256 0.087255 0.066714 0.255905 1.000000 0.132138 0.046615 0.095802 -0.057914
Steiermark 0.006857 -0.166972 0.162104 0.269141 0.132138 1.000000 0.088051 0.084287 -0.190364
Tirol 0.072319 0.195124 0.071057 0.075858 0.046615 0.088051 1.000000 0.135859 0.100921
Vorarlberg -0.122540 0.020510 0.053398 0.066559 0.095802 0.084287 0.135859 1.000000 0.080920
Wien 0.120325 0.270078 0.356979 0.051265 -0.057914 -0.190364 0.100921 0.080920 1.000000